Apparatus and associated methods in relation to carbon nanotube networks

ABSTRACT

In one or more embodiments described herein, there is provided an apparatus comprising a substrate, and a plurality of carbon nanotubes (semiconducting nano-elements) disposed and fixed with said substrate. The nanotubes are disposed and fixed on said substrate such that they define a carbon nanotube network substantially at the percolation threshold of the network. As the network is at the percolation threshold, this provides for one or more signal paths extending from an input region to an output region. The apparatus is configured to, upon receiving particular input signalling via the input region, provide particular predefined output signalling at the output via the one or more signal paths, the particular output signalling being predefined according to the one or more one signal paths.

TECHNICAL FIELD

The present disclosure relates to the field of carbon nanotube networksand methods, computer programs and apparatus associated with suchnetworks. Certain disclosed aspects/embodiments relate to portableelectronic devices, in particular, so-called hand-portable electronicdevices which may be hand-held in use (although they may be placed in acradle in use). Such hand-portable electronic devices include so-calledPersonal Digital Assistants (PDAs).

The portable electronic devices/apparatus according to one or moredisclosed aspects/embodiments may provide one or more audio/text/videocommunication functions (e.g. telecommunication, video-communication,and/or text transmission (Short Message Service (SMS)/Multimedia MessageService (MMS)/emailing) functions), interactive/non-interactive viewingfunctions (e.g. web-browsing, navigation, TV/program viewing functions),music recording/playing functions (e.g. MP3 or other format and/or(FM/AM) radio broadcast recording/playing), downloading/sending of datafunctions, image capture function (e.g. using a (e.g. in-built) digitalcamera), and gaming functions.

BACKGROUND

A problem that occurs when trying to achieve nano-computing systems isto find system architectures that can be manufactured in practice. Forexample, manufacturing of nanoscale components is inherently unreliableand leads to large amounts of failed and poorly functioning devices. Thearchitecture, the computing logic and the algorithms of such devicesneed to take this into account, and to be able to compensate for thisunreliability somehow.

At present there are two basic options on how to solve this: try toimprove hardware reliability by some means (e.g. via manufacturingprocess, redundant components), or to use software that can deal withunreliable hardware (e.g. via error correction, dynamic reconfigurationof hardware, robust algorithms or computing principles, like neuralnetworks or any other machine learning algorithms capable of learning orbeing taught). Unfortunately, there is always a trade-off betweenhardware and software complexity: with simple software the hardware hasto be very reliable, and with unreliable hardware the software requiredcan be extensively complex.

One data processing device architecture that has been broadly usedoutside nano-scale implementation is the cross-bar architecture.Cross-bar structures have been proposed in U.S. Pat. No. 6,128,214“Molecular wire cross-bar memory”, with certain types of binary statemolecules in between, which can be tune on or off by applying voltageacross the nanowires. Also, papers by Likharev cover similar ideas indevices that utilise neural networks. Furthermore, US20030236760“Multi-layer training in a physical neural network formed usingnanotechnology” and US2009/0043722 A1 “Adaptive neural network utilizingnanotechnology-based components” describe a neural-network typeinformation processing devices utilizing nanoparticles. The devicesdescribed in this document consists of a cross-bar structure, withnanoparticles diluted in a solvent in between the top and bottom partsof the sandwiched structure.

WO 2009/013754 “Chemically sensitive field effect transistors and usethereof for electronic nose devices” describes how to use siliconnanowire field effect transistors (FETs) as chemical sensors, inconjunction with pattern recognition algorithms to detect the sensedchemicals. Here the pattern recognition algorithms are implemented intraditional CMOS devices

This same structure has been proposed for nano-implementation. The ideais that by crossing nanowires and functionalizing the crosspoint somehowwith suitable molecules one can get a two-terminal transistor, which canbe turned on and off by applying voltage to the wires. But there isstill a problem that the system needs to interface at the microscale, somicroscale connections to the nanowires are needed.

However, as stated above, this is difficult to do. Manufacturing canbecome complicated, and there are often losses due to poor contacts. Inparticular, the operation of the molecules in the cross-points is verylimited, and only simple operations can be realized. There are alsoproposals of using cross-bar structures to perform Boolean computing(i.e. as FPGA processors) and as neural networks. With FPGA, the problemcomes with the very large area required to construct even a simplecircuit. The neural network-type systems suffer from the fact that (dueto the cross-bar structure) the number of connections between thedifferent logic gates or “neurons” are quite limited, which reduces thecomputing capability of the system. The number can be enhanced by addingmicroscale wires on top, but this complicates the manufacturing process.Due to the complications mentioned, no functioning cross-bar circuitshave been demonstrated to date.

Another problem comes with effective analysis of sensory data. Nanoscalesensor elements have been fabricated from many materials, but the dataneeds to be analysed somehow to extract relevant features. Digital CMOSdoes not provide the best way of doing this, since it requires analog-todigital conversions, and can thus be very energy consuming compared tothe energy used by the nanoscale sensor, particularly when the sensordata is very complex as its analysis can require a lot of processing.

The listing or discussion of a prior-published document or anybackground in this specification should not necessarily be taken as anacknowledgement that the document or background is part of the state ofthe art or is common general knowledge. One or more aspects/embodimentsof the present disclosure may or may not address one or more of thebackground issues.

SUMMARY

In a first aspect, there is provided an apparatus comprising:

-   -   a substrate;    -   a plurality of carbon nanotubes disposed and fixed with said        substrate to define a carbon nanotube network substantially at        the percolation threshold of the network, wherein the        percolation threshold provides for one or more paths extending        from an input region to an output region of the network,    -   the apparatus being configured to, upon receiving particular        input signalling via the input region, provide particular        predefined output signalling at the output via the one or more        signal paths, the particular output signalling being predefined        according to the one or more one signal paths.

The carbon nanotubes of the network may be disposed on a (one or more)substrate(s), e.g. adhered to the top of a silicon substrate/substratum.It is simple, easy and straightforward to apply nanotubes on to existingstructures/materials using this method. Alternatively, the nanotubes maybe disposed within an equivalent insulating/non-conductive/semiconductormaterial substrate, e.g. suspended in an epoxy, epoxy resin, or othersuch material. Impregnating the network within such materials canthereby provide additional strength, support and robustness to thenetwork.

The carbon nanotubes may be disposed randomly on said substrate todefine said network. Thereby, the particular predefined outputsignalling may comprise unique output signalling provided by virtue ofthe random distribution of the carbon nanotube network.

The apparatus may provide, via the random disposition of the network andunique output signalling, an apparatus that can act as a physicallyunclonable function. This may be by virtue of its nanoscale (inherent inthe size of carbon nanotubes) size network, and/or the difficultyinherent in replicating such structures (random, ordered, or otherwise)accurately on the nanoscale.

The input region may be configured to receive an inputted challengesignal (as per a challenge-response pair in physically unclonablefunctions) and the output may be configured to provide the resultantresponse signal in reaction to the inputted challenge signal, theinputted challenge signal being altered by the signal path or paths ofthe carbon nanotube network.

The apparatus may further comprise one or more gates configured to be inelectrical communication with the carbon nanotube network disposed onthe substrate, the apparatus also being configured to be capable ofapplying bias to one or more of said gates to alter the electricalproperties of the carbon nanotube network to provide particularpredefined output signalling according to the respective bias applied toone or more of the gates.

The apparatus may further comprise one or more input connections and oneor more output connections, the input and output connections each beingrespectively configured to be in electrical communication with the inputand output regions of the carbon nanotube network, the one or moresignal paths of the network being configured to extend betweenrespective input connections and corresponding output connections,

-   -   the apparatus being configured to, upon receiving particular        input signalling via one or more of the input connections,        provide particular predefined output signalling at one or more        of the corresponding output connections connected via the one or        more signal paths, the particular output signalling being        predefined according to the one or more signal paths connecting        the respective input connections and corresponding output        connections.

The one or more of the signal paths may be respectively configured toprovide one or more corresponding functions, the particular outputsignalling being predefined according to the one or more functionsprovided by the one or more respective signal paths.

Each of the functions may be selected from a list of analog functions,or digital/logic functions, or a combination of the two.

The analog functions may be any mathematically describable functioninvolving the inputs. For example, the functions may be representedmathematically by: x^(n), +n, −n, /n, ×n, etc, or any combinationthereof (where n is any real or imaginary number, integer or otherwise).The analog functions may correspond to a solution of a mathematicalproblem or operation of a computational or mathematical model oralgorithm. For example, the functions may act on input signalling tocalculate a Fourier transform, or an integration/differentiationcalculation, etc. The mathematical operation may also utilisemathematical operations that involve one or more different components ofthe input signalling, e.g. S1/S2, S1×S2, S1+S2, etc, where S1 is a firstcomponent and S2 is a second component of the input signalling.

The logic functions provided may be selected from the list of: AND, OR,XOR, NOR, NOT, or any combination thereof. The logic functions may alsobe a simple ‘YES’ connection to allow signals to pass unaffected, or a‘NO’ connection which does not allow signals to pass through the pathproviding this function.

The apparatus may also be configured to be capable of applying bias toone or more of said gates to alter the electrical properties of thecarbon nanotube network to thereby configure/reconfigure one or morepaths to provide one or more respective functions, the particularpredefined output signalling being predefined according to the one ormore provided/reconfigured functions.

The apparatus may also be configured to be capable of selectivelyswitching/altering the bias applied to any one or more of the gates tocause the function provided by any one or more of the correspondingsignal paths to be switched from a first function to a second logicfunction, wherein the second function is different from the firstfunction.

The gates may be configured to be switchable from a second function to afurther function. The gates may be configured to be rapidly switchedfrom one function to another. A function provided by one or more of thesignal paths may comprise two or more functions that operate differentlyon distinct signalling. For example, a given signal path may perform onefunction on a first component of the input signalling, whilst the samesignal path may perform a second different function on a secondcomponent of the input signalling. For example, a high frequencycomponent of input signalling may experience a first (e.g. AND) functionacross a given signal path, whilst a low frequency component of theinput signalling may experience a different (e.g. OR) function acrossthe same signal path. The operation of these functions may occur insequence, simultaneously, or they may temporally overlap one another.

The apparatus may be configured to be capable of controlling the one ormore gates to configure itself as an application specific integratedcircuit (ASIC).

The apparatus may also comprise a processor electrically connected toone or more of the gates, the input region and the output region,wherein the processor is provided with a learning algorithm configuredto be able to perform the method of:

-   -   detecting logic functions provided by one or more signal paths        of the network,    -   calculating the applied bias needed to        provide/configure/reconfigure one or more of the signal paths to        provide one or more desired logic functions, and    -   applying the necessary bias to the corresponding gates to        configure the respective one or more signal paths to provide the        one or more desired logic functions.

The apparatus may also comprise adaptive learning circuitry that isconfigured to be able to actively tune the functions and functionalityprovided by the network based on desired parameters. For example, auser/manufacturer may desire the apparatus to perform a specificintended purpose. In one embodiment, by inputting the desired functionof the apparatus into the adaptive learning circuitry, the circuitrymay, by changing the applied bias, alter the functions provided by thenetwork to achieve an apparatus whose paths provides the necessaryfunctions to achieve the intended purpose for the chip, e.g. a 555timing chip, standard ICs, a calculator, a CPU for a computer, a sensorchip for a sensing device (e.g. detecting chemicals, smells, images,etc), a pattern recognition chip for such devices (a chip that canidentify or recognise patterns in provided input signalling), or even adata classification chip for such devices (a chip that cansort/order/classify data provided via the input signalling), etc.

According to a further aspect, there is provided a radio frequencyidentification tag comprising the apparatus of the first aspect, whereinthe input region is electrically connected to an input antenna, and theoutput region is electrically connected to an output antenna, the radiofrequency identification tag being configured to, upon receivingparticular input signalling via the input antenna, provide uniqueparticular predefined output signalling via the output antenna.

According to a further aspect, there is provided a nano-scale processorcomprising the apparatus of the first aspect.

According to a further aspect, there is provided a transistor comprisingthe apparatus of the first aspect, wherein the input region acts as asource, the output region acts as a drain, and the network together withthe substrate acts as a semiconductor material provided between thesource and the drain.

The transistor may be a crossbar structure transistor.

In a further aspect, there is provided an apparatus comprising:

-   -   means for receiving a network of means for conduction on the        nano-scale; and    -   means for conduction on the nano-scale, said means for        conduction disposed and fixed with said means for receiving a        network of means for conduction to define a network of means for        conduction on the nano-scale that it is at the percolation        threshold of the network, wherein the percolation threshold        provides for one or more paths extending from a means for        inputting to a means for outputting of the network,    -   the apparatus being configured to, upon receiving particular        input signalling via the means for inputting, provide particular        predefined output signalling at the means for outputting via the        one or more signal paths, the particular output signalling being        predefined according to the one or more one signal paths.

According to a further aspect, there is provided a method for assemblingan apparatus, the apparatus comprising:

-   -   a substrate;    -   a plurality of carbon nanotubes disposed and fixed with said        substrate to define a carbon nanotube network substantially at        the percolation threshold of the network, wherein the        percolation threshold provides for one or more paths extending        from an input region to an output region of the network,    -   the apparatus being configured to, upon receiving particular        input signalling via the input region, provide particular        predefined output signalling at the output via the one or more        signal paths, the particular output signalling being predefined        according to the one or more one signal paths, wherein the        method comprises:    -   disposing the plurality of the carbon nanotubes with the        substrate such that the network is substantially at its        percolation threshold, the percolation threshold thereby        providing for the one or more paths extending from the input        region to the output region.

The step of disposing the carbon nanotubes may also comprise disposingthe nanotubes randomly on said substrate to define said network, suchthat particular predefined output signalling comprises unique outputsignalling provided by virtue of the random distribution of the carbonnanotube network.

The method of assembly may also comprise the step of providing one ormore gates such that they are in electrical communication with thecarbon nanotube network disposed on the substrate.

According to a further aspect, there is provided a method forconfiguring an apparatus, the apparatus comprising:

-   -   a substrate;    -   a plurality of carbon nanotubes disposed and fixed with said        substrate to define a carbon nanotube network substantially at        the percolation threshold of the network, wherein the        percolation threshold provides for one or more paths extending        from an input region to an output region,    -   the apparatus being configured to, upon receiving particular        input signalling via the input region, provide particular        predefined output signalling at the output via the one or more        signal paths, the particular output signalling being predefined        according to the one or more one signal paths; wherein the        method comprises:    -   detecting logic functions provided by one or more signal paths        of the network,    -   calculating the applied bias needed to        provide/configure/reconfigure one or more of the signal paths to        provide one or more desired logic functions, and    -   applying the necessary bias to the corresponding gates to        configure the respective one or more signal paths to provide the        one or more desired logic functions.

According to the method aspect immediately above, there is provided acomputer readable medium comprising computer code configured to performthe method of the aspect immediately above, when run on a processorcomprised by the apparatus.

According to a further aspect, there is provided an apparatuscomprising:

-   -   a substrate;    -   a plurality of semiconducting nano-elements disposed and fixed        with said substrate to define a semiconducting network of        nano-elements substantially at the percolation threshold of the        network, wherein the percolation threshold provides for one or        more paths extending from an input region to an output region of        the network,    -   the apparatus being configured to, upon receiving particular        input signalling via the input region, provide particular        predefined output signalling at the output via the one or more        signal paths, the particular output signalling being predefined        according to the one or more one signal paths.

The present disclosure includes one or more corresponding aspects,embodiments or features in isolation or in various combinations whetheror not specifically stated (including claimed) in that combination or inisolation. Corresponding means for performing one or more of thediscussed functions are also within the present disclosure.

Corresponding computer programs for implementing one or more of themethods disclosed are also within the present disclosure and encompassedby one or more of the described embodiments.

The above summary is intended to be merely exemplary and non-limiting.

BRIEF DESCRIPTION OF THE FIGURES

A description is now given, by way of example only, with reference tothe accompanying drawings, in which:

FIG. 1 a shows an isometric view of a substrate having a carbon nanotubenetwork fixedly disposed with the substrate as used in severalembodiments of the invention.

FIG. 1 b shows a top-down schematic view of the substrate of FIG. 1 a.

FIG. 1 c shows a graph illustrating the nature of the percolationthreshold of a given network formed by a given number of paths with agiven length, the network having a given density.

FIG. 1 d shows a photo of such a substrate as depicted in FIG. 1 a.

FIG. 2 a shows a top-down schematic view of a first embodiment utilisingthe substrate of FIG. 1 a.

FIG. 2 b shows a variation of the first embodiment utilising a crossbarstructure transistor.

FIG. 2 c shows a further variation of the first embodiment utilised in aradio frequency identification (RFID) tag.

FIG. 3 a shows a top-down schematic view of a second embodimentutilising the substrate of FIG. 1 a.

FIG. 3 b shows an illustration of active paths across the network in thesecond embodiment.

FIG. 3 c shows a representation of the mapping of the logical structure(or the computational model) of the apparatus and paths through thephysical network of the second embodiment.

FIG. 3 d shows a further representation of the mapping of the logicalstructure (or the computational model) of the apparatus and pathsthrough the network of the second embodiment.

FIGS. 3 e & 3 f show side views of alternative methods of laying thecarbon nanotube network on the substrate in the first and secondembodiments.

FIG. 3 g shows an example of how to create variations of the first andsecond embodiments in a circular architecture.

FIG. 4 shows a flowchart illustrating the method of tuning the gates inthe second embodiment.

FIG. 5 shows a flowchart illustrating the method of assembly of thesubstrate.

FIG. 6 illustrates schematically a computer readable media providing aprogram according to an embodiment of the present invention.

DESCRIPTION OF SPECIFIC ASPECTS/EMBODIMENTS

In one or more embodiments described herein, there is provided anapparatus comprising a substrate, and a plurality of carbon nanotubesdisposed and fixed with said substrate. The nanotubes are disposed andfixed on said substrate such that they define a carbon nanotube networksubstantially at the percolation threshold of the network. As thenetwork is at the percolation threshold, this provides for one or moresignal paths extending from an input region to an output region of thenetwork. The apparatus is configured to, upon receiving particular inputsignalling via the input region, provide particular predefined outputsignalling at the output via the one or more signal paths, theparticular output signalling being predefined according to the one ormore one signal paths. This provides for a series of signal paths on theorder of nanometres by virtue of the scale and properties of the carbonnanotubes forming the signal paths. The nature of the carbon nanotubesand the formation of such a network on such a small scale allows otheradvantages to be gained in certain embodiments.

For example, when disposing such networks there is often a great deal ofuncontrollable random changes in the final product. In a firstembodiment, random signal paths are provided in the network. As such,when particular signalling is input into the input region, outputsignalling will be produced at the output region, with characteristicsdetermined by the route taken along the one or more signal paths of thenetwork. As the paths of the network are completely random, and theroute taken is entirely dependent on the physical nature of the networkdisposed with the substrate, it is very difficult to copy and replicatethe structure responsible for providing the particular outputsignalling. This therefore provides what is known as a physicallyunclonable function. This is a device that produces a reaction signal inresponse to a challenge signal that, by virtue of its randomisedphysical design, is difficult to replicate or fake.

In other examples, such uncontrollable random variations areundesirable. However, it is still desirable to create working nanoscalenetworks using carbon nanotubes, as they can be used to developnanoscale processors. The uncontrollable random path variationsintroduced via the manufacturing process result in different functions(e.g. AND, OR, XOR logic functions, etc) being provided by differentpaths within the network. These functions are affected by the electricalproperties of the network. In a second embodiment, by altering theseinitially random electrical properties post-manufacture, it is possibleto configure/reconfigure the electrical properties of existing pathwaysto provide a different desired function instead of their initialinherent function. This can be done via applying controlled biases tospecific points in the network. In a further embodiment, a learningalgorithm that ‘tests’ the inherent properties of the network andexperiments on what biases placed where will elicit what function canenable designers to achieve desired functions from a network, despitemanufacturing errors. This provides a way to ‘correct’, ‘tweak’ or‘tune’ the inherent random errors introduced into such networks byvirtue of their size and to control their electrical properties, or evento completely customise/tailor a network for a particular specific taskor functionality.

FIG. 1 a shows an isometric view of the abovementioned substrate 1 inapparatus 100 and FIG. 1 b shows a top-down schematic view of saidapparatus 100. This substrate 1 is the basic substrate that is used tobuild the first and second embodiments of the present invention. We willtherefore describe this substrate 1 first before describing the otherembodiments, so that its structure and inherent properties are clearlyunderstood.

The substrate 1 comprises a plurality of carbon nanotubes 2. Thenanotubes 2 define a carbon nanotube network 2. The network 2 has anumber of signal paths extending throughout itself by virtue of theinterconnected nanotubes within the network 2.

The network 2 is provided within the substrate 1. In this particularillustration the substrate 1 is formed from an insulating epoxy resinthat has been used to seal the carbon nanotubes 2 within itself. Duringmanufacture, the network 2 is first formed from the plurality of carbonnanotubes. These are then placed in a suitable mold and the epoxy resinis injected into the mold to engulf the network 2. Once the epoxyhardens, the substrate 1 is then formed, sealing the network 2 withinthe substrate 1. It is important that the substrate 1 is formed from anon-conductive insulating material to ensure that the signal paths ofthe network are able to function unimpeded, as well as being a materialthat provides structural integrity to the network 2.

It should be noted that whilst the embodiments described below aredirected towards semiconducting networks utilising carbon nanotubes, theskilled person will appreciate that any semiconducting material made toform semiconducting elements on the nanoscale (i.e. nano-elements) canbe utilised to form the necessary network. For example, particles may beused, as well as other types of nano-scale wires, or even smallsemiconducting plates could be used to implement this network. However,the following embodiments specifically focus on the carbon nanotubeimplementation of this network.

The skilled person will also appreciate that other variations arepossible in other embodiments. For example, the substrate may also be asilicon board with the nanotubes simply placed and fixed on top of theboard. In such an example, the network can be formed and affixeddirectly onto a silicon board or other similar prefabricated insulatingsubstrate. In still another embodiment, the network is disposed onto thesurface of a substrate and is held together by the Van der Waals forcebetween each of the nanotubes. This force would hold the individualnanotubes together to form a network on the surface of the substrate.Therefore, when we describe the network as being disposed or provided“with” the substrate, we mean that the network can be provided “within”the substrate, or “on” the substrate, or the like.

It should be noted that in each of these examples we have describedthere has been one substrate. In other embodiments (not shown or furtherdescribed), the network is disposed on/with a plurality (two or more) ofsubstrates and not just one substrate.

Referring again to FIGS. 1 a & 1 b, the network 2 extends across andthroughout the substrate 1. The arrangement of the network 2 with thesubstrate 1 defines an input region 3 at the left end of the substrate,and an output region 4 at the right end of the substrate (relative toFIG. 1 b). The signal paths of the network extend from the input region3 to the output region 4. In order to allow for input signalling to beprovided and output signalling to be received, the nanotubes of thenetwork 2 located near these ends can be connected electrically toinput/output connections respectively. How this is accomplished will bediscussed later with regard to specific embodiments.

During manufacture, the nanotubes 2 are disposed to form the network 2such that the network is at or above what is known as the “percolationthreshold” (also referred to as percolation density—ρ_(c)). FIG. 1 cshows an illustration of what the percolation threshold is and how it isreached via the configuration/disposition of any given network.

For a given network, the percolation threshold is the point at which thenetwork achieves long range connectivity. This means that when a networkis at its percolation threshold, there is at least one path along whichit is possible to smoothly travel from one side of the network to theother, thus providing long range connectivity in the network. Below thisthreshold there is typically only short range connectivity, in thatthere are only short paths that can be taken. These short paths do notprovide a route from one side of the network to the other. Above thisthreshold, more paths/routes from one side of the network to the otheroccur, such that there are multiple routes that can be taken.

The connectivity of the network is determined by a number of factors:

-   -   (1) path length—for a particular network density, the longer        each average path length is (relative to the size of the        network), the closer the network will be to the percolation        threshold;    -   (2) network density—for a particular average path length, the        more paths there are per unit volume, the closer the network        will be to the percolation threshold.

These factors are dependent on one another. The shorter the average pathlength, the higher the network density needs to be to ensure that theshort paths form at least one long range connected route across thenetwork. Similarly, the lower the network density, the longer the pathsneed to be in order to ensure that there is at least one long rangeconnected route across the network.

In this particular illustration (with reference to FIGS. 1 a & 1 b), thepercolation threshold represents the density transition point from whichthe system can be seen to move from a network of substantiallyindividual carbon nanotubes, to a single connected large cluster towhich nearly all of the carbon nanotubes belong. Well above thisthreshold, the network becomes thick and very dense. For example, for asquare sample of side length L, and carbon nanotubes of length l, thepercolation density ρ_(c) is given by the formula:

ρ_(c) =N/(L/l)²=5.6±0.1

Therefore, the threshold number of carbon nanotubes is:

N=5.6×(L/l)²

This value naturally changes when the system is not a square, but e.g. arectangle or a circle. Also, as stated above, the length of thenanotubes as well as the length distribution of the carbon nanotubesaffects the threshold.

There are at least three clear advantages in the network being at orslightly above the percolation threshold:

-   -   (1) The density variation in the carbon nanotube network is at a        maximum—Well below ρ_(c), the carbon nanotube network is not        connected but consists of individual carbon nanotubes. Also, the        paths from the input/source to the output/drain may not be        connected. On the other hand, well above ρ_(c), the carbon        nanotube network is dense and behaves similarly from an area to        another, i.e. like a bulk material. Thus the largest variation        between randomly connected transistors can be achieved at the        threshold.    -   (2) When at the threshold, there can be found so called “red”        carbon nanotubes of the known “links-and-blobs” model (described        in D. Stauffer and A. Aharony, Introduction to Percolation        Theory (Taylor & Francis, London, 1991)). These “red” carbon        nanotubes are such that neglecting or breaking one of them can        essentially break the network into two parts. This is a        consequence of the large density variation of the random carbon        nanotube network at the ρ_(c).    -   (3) In general ⅓ of carbon nanotubes are metallic and ⅔ are        semiconducting—Ideally, there should not be a path following        carbon nanotubes which are only metallic. This creates problems        as there will be one preferred path, effectively simply        providing a wire connection, rather than a network of small        semiconducting wires with tunable electrical properties creating        multiple path choices. The probability of metallic paths from        input/source to output/drain is low at the percolation        threshold, but increases above it.

It would be advantageous to configure the nanotube network such that itis substantially at the percolation threshold. By the network being“substantially at the percolation threshold”, it is meant that thenetwork density and/or nanotube lengths are such that the network iseither at the percolation threshold, or slightly above it. For example,this could be by 0.01%, 0.1%, 1%, 2%, 3%, 4%, 5%, 10%, 15%, 25%, 50%,100% or even 200% above the percolation threshold, or any combination ofranges therebetween. It should be noted that the long range connectivityeffects that begin to occur at the percolation threshold can still bepresent in the network even when the network is at a value that is up tothree times the percolation threshold value (i.e. 200% above it). Whilstthese effects can still be present, it can become more difficult to tunethe properties of the network, in that it can be more difficult toaffect the selected signal path, and the strength of the signal paththat the signalling is travelling along, from the input region to theoutput region than when the network is close to the percolationthreshold value. Also, variations between manufactured devices, orvariations between one part/region of a device to another, can decreasethe further the network is from the percolation threshold. This is atleast partly due to the number of possible long range connective pathsincreasing as the density of the network increases. This can alsopresent a difficulty similar to point (3) above (for example, signalsmay pass through the network unaffected by any bias applied to effectthe electrical tuning of the network).

In the substrate 1 shown in FIG. 1 a, and in the following embodiments,the network 2 is configured via its disposition to be at or above thepercolation threshold (as discussed above). The nanotubes 2 of thenetwork 2 are of sufficient number and sufficient length to ensure longrange connectivity from one side of the provided network to the other.This is to ensure that the network has at least one signal path that canallow signalling to be sent from one side of the network to the otheralong said signal paths.

Please note that the graph depicted in FIG. 1 c is shown merely toillustrate the percolation threshold. This graph is not intended toaccurately graph the connectivity of networks such as the presentinvention, since at the percolating threshold of the network the pathlength of signalling crossing the network would be much greater than thewidth of the network, since the path taken would be far from straight.This is merely to illustrate that there is a threshold point at whichlong range connectivity begins and the network can be treated as a fullyconnected network along at least one path.

FIG. 1 d illustrates a particular implementation of this carbon nanotubenetwork in the form of a transistor. There are palladium source anddrain electrodes provided (Pd—note that in this example the electrodelabelling can be considered to be reciprocal, in that either electrodecan the source or drain electrode), and the carbon nanotube network isdisposed between them. The electrodes are both connected to the inputand output regions of the network 2 to allow for input signalling to beprovided and transmitted into the network 2, and to allow for outputsignalling to be received from the network 2 as a result of the providedinput signalling.

When input signalling is applied to the source electrode, the signalwill take a particular path through the network to the drain electrode.The path taken by the input signalling will affect the input signalling,thereby changing it and altering it to provide particular predefinedoutput signalling. The output signalling is in response to the inputsignalling, and has characteristics that are by virtue of thedisposition/arrangement of the nanotube network 2 with the substrate.Described herein are further embodiments that take advantage of theproperties of such a disposed carbon nanotube network to achieve aparticular technical outcome. These will now be described in more detailwith reference to the figures.

Embodiments depicted in the figures have been provided with referencenumerals that correspond to similar features of earlier describedembodiments. For example, feature number 1 can also correspond tonumbers 101, 201, 301 etc. These numbered features may appear in thefigures but may not have been directly referred to within thedescription of these particular embodiments. These have still beenprovided in the figures to aid understanding of the further embodiments,particularly in relation to the features of similar earlier describedembodiments.

We will now discuss a first embodiment of the invention utilising thissubstrate with reference to FIG. 2 a.

This apparatus 200 of the first embodiment utilises a substrate 201provided with a network 202 like the apparatus 100 of the firstembodiment. The apparatus 200 also comprises input connections 203 b andoutput connections 204 b. In this particular embodiment, theseconnections 203 b, 204 b are short terminal pins that are to be insertedinto the setting epoxy resin forming the substrate 201 during themanufacturing stage of the apparatus 200. The input connections 203 bare to be electrically connected to the nanotubes 202 located near theinput region 203, and the output connections 204 b are to beelectrically connected to nanotubes 202 located near the output region204. The skilled person will also appreciate that other variations arewithin the scope of the present invention. For example, the connections203 b, 204 b may be conductive tracks laid down on a silicon board thatthe network is to be disposed on, the conductive tracks to beelectrically connected to the nanotubes of the respective regions 203,204. In other variations, the nanotubes located near the input/outputregions are actually exposed from the substrate to allow theconnections/terminals 203 b, 204 b to simply be electrically connectedto the exposed nanotubes without the need for insertion of suchterminals into the setting epoxy resin substrate 201.

As in apparatus 100, the network 202 is again configured to be at orabove the percolation threshold for this network to achieve long rangeconnectivity across the network 202. The substrate 201 is similarlyformed from an epoxy resin, and the network 202 is disposed randomlywithin the epoxy resin substrate 201 during manufacture.

During manufacture, the input and output connections 203 b, 204 b areinserted into the respective input and output regions 203, 204 duringsetting of the epoxy resin. They are inserted such that they are inelectrical communication with the nanotubes of the network 202 locatednear the input/output regions 203/204. The electrically connected inputconnections 203 b allow for input signalling to be provided via theinput region to the signal paths of the network 202. Similarly, theelectrically connected output connections 204 b allow for any outputsignalling coming from the network to be outputted to further devices.

A signal provider (not shown) such as an oscilloscope, computer/CPU,input antenna, or the like (i.e. anything that can provide an electricaloutput to provide for input signalling) can be connected to the inputconnections 203 b to provide such signalling. A separate output device(not shown) such as an oscilloscope, computer/CPU, output antenna, orthe like can also be connected to the output connections 204 b to allowfor output signalling to be taken from the network 202 and provide anoutput. This can enable the apparatus to act as a sensing apparatus, apattern recognition apparatus, a data classification apparatus, etc.

We will now describe the inner function of this apparatus.

As the input connections 203 b are electrically connected to the carbonnanotubes of the network 202, the input signalling is transmitted acrossthe network 202 by virtue of the network 202 being at/above thepercolation threshold. The network 202 will have an effect on theprovided input signalling due to its inherent electrical properties. Theexact effect that the network 202 has on the signalling is dependent onthese electrical properties. These properties are, in turn, heavilydependent on the physical arrangement of the nanotubes within thenetwork 202 and the network 202 itself. By arranging the network 202randomly during the manufacturing stage, the exact result that thenetwork 202 will have on the input signalling will also be entirelyrandom and therefore unique to that particular network 202.

This apparatus 200 can therefore be seen to provide a device that willproduce particular output signalling in response to particular inputsignalling. This output signalling will be predefined according to theuniquely random disposition and arrangement of the network 202 and itssignal paths. The unique response characteristics of the network 202 aredifficult to replicate by virtue of its randomised arrangement, as wellas the fact it is on a size scale that is difficult to examine. Thescale on which the network 202 has been manufactured also makes itexceedingly difficult to accurately reproduce exact copies of knownapparatus without erroneous or failed results. This provides what isknown as a physically unclonable function: a device/apparatus thatoperates on a given input signal (termed a ‘challenge’ signal) toprovide a particular corresponding and predefined output signal (termeda ‘response’ signal) that is dependent on its physical attributes, butthat is also difficult to replicate (hence the term ‘unclonable’).

Therefore, this apparatus can be used to uniquely identify objects in asecure fashion. For example, for a given apparatus receiving known inputsignalling, the output signalling given out in response to the inputwill be known. It is therefore easy to identify a particular apparatusby evaluating such output signalling produced in response to inputsignalling. This makes it easy to evaluate the identity ofapparatus/devices, and whether they are fake or genuine. This is asecure method, as the apparatus is very difficult to forge due its smallscale and randomised arrangement.

In a variation of this embodiment, the apparatus 200 is implemented as atransistor (as per FIG. 1 d), with the input connections 203 b acting asa source electrode, and the output connections 204 b acting as a drainelectrode. The network 202 and substrate 201 act as the semiconductormaterial sandwiched between the source and drain electrodes as pernormal transistor design.

We will now describe a further variation on this embodiment withreference to FIG. 2 b. In this embodiment, the apparatus 210 isimplemented in a transistor, specifically a cross-bar structuretransistor. A crossbar structure transistor utilises a signal pathcrossed by two control lines, separated by a switchable junction(typically on the nanoscale). Such a structure provides a matrix ofcarbon nanotube Field Effect Transistors (FET). Each of these can beindividually addressed, or they can be addressed in groups.

In this embodiment, the apparatus 210 comprises the network andsubstrate as in apparatus 100 of the first embodiment. However, theinput and output regions 213, 214 are both positioned on the top surfaceof the network/substrate.

The apparatus 210 also comprises an insulator 217 d (specifically aninsulating layer), source electrodes 213 b (individually referred to asSe1 . . . n), drain electrodes 214 b (individually referred to as De1 .. . n) and gate electrodes 216 (individually referred to as Ge1 . . .n). The apparatus further comprises a processor (not shown) connected tothe gates 216 and a power source (not shown). The processor isconfigured to control the application of a bias to one or more of thegates 216 to select specific cross-points within the transistor.

In this embodiment, the insulator 217 d is a preformed silicon wafer. Inother embodiments it is a chemically deposited insulating material, suchas a carbon based epoxy. In still other embodiments, it is an epoxyresin that can be applied and left to set during manufacture. Theskilled person will appreciate that there are other alternatives thatare also within the scope of this invention.

The source, drain and gate electrodes 213 b, 214 b, 216 are, in thisembodiment, formed from gold strands, but the skilled person willappreciate that they can be formed from any electrically conductivematerial suitable for such a cross-bar transistor architecture. Forexample, silver, copper, palladium, or even meshes of graphene or weavesof carbon nanotubes.

The source and drain electrodes 213 b, 214 b are to be electricallyconnected to the nanotubes of the network 212 as per the input/outputconnections 203 b, 204 b of the apparatus 200 of the first embodiment.As they are separate strands they are capable of providing substantiallyseparate input and output connections to the plurality of signal pathsof the network 212.

As in apparatus 100, the network 212 is again configured to be at orabove the percolation threshold for this network to achieve long rangeconnectivity throughout the network 212. The substrate 211 is similarlyformed from an epoxy resin, and the network 212 is disposed randomlywithin the epoxy resin substrate 211 during manufacture.

The source and drain electrodes 213 b, 214 b are electrochemicallydeposited onto the network/substrate 201, 202 arrangement so as to beelectrically connected to the network 212 via the input/output regions213, 214 positioned on top of the network/substrate 201, 202.

The insulating layer 217 d is deposited on top of the source and drainelectrodes 213 b, 214 b.

Finally, the gate electrodes 216 and deposited on top of the insulatinglayer 217 d. This forms the sandwiched cross-bar structure transistor ofthis embodiment.

We will now describe the functionality of this embodiment.

This architecture configures the nanotube network 212 to be a network ofFETs that are each individually addressable via the separate inputoutput connections of the source and drain electrodes 213 b, 214 b andthe plurality of gate electrodes 216. This is specifically achieved byapplying a bias to one or more of the gates Ge1 . . . n. When such abias is applied, certain paths are selected within the network 212, andtheir corresponding input/output connections (via source and drainelectrodes 213 b, 214 b/corresponding gates Se1 . . . n & De1 . . . n)are also selected. The configuration of the connected processor allowsit to apply such a controlled bias to specific gates 216. This opens upspecific pathways within the transistor network. Therefore, theapplication of a controlled bias allows the selection of specificcross-points of the network 212, formed by the gates 216 and the sourceand drain electrodes 213 b, 214 b within the transistor. Note that theinsulating layer 217 d is necessary so that the applied bias is notdirectly electrically conducted to the source and drain electrodes 213b, 214 b.

By implementing the first embodiment in this way, it is possible toprovide a network of FETs that can be individually addressed in a numberof different ways. For example, each FET could be addressed as anindividual transistor, or smaller sub-groups could be formed, orspecific patterns could be selected from within the network 212. Bycontrolling the specific application of bias across the gate electrodes216, it is possible to configure this apparatus 210 to provide a numberof different response signals in reaction to a number of differentchallenge signals (as per the challenge-response pairs discussed above).

The network provides a challenge-response area with a large space forreceiving challenges. The number of challenges that can be received isinfluenced by the number of gate electrodes provided. The number ofpossible challenges is:

Number of challenges=m×2n;

n—number of gate electrodes

m—number of parameter levels measured

For example, the resistance of each signal path and/or source-draincurrent via the transistor at fixed gate voltage and/or any othertransistor parameter can be used as informative parameters in responseto signalling that can be input via the abovementioned electrodes.

The challenge-response pairs can therefore be seen to correspond to thetransistor parameters. For example, gate electrode (V_(g)) andsource-drain (L_(sd)) can be linked such that there are characteristicpairs for every manufactured apparatus, in that a given apparatus hasmultiple, measurable V_(g)-L_(sd) pairs that are sufficiently differentfrom every other apparatus such that each device can be considered tohave a unique “fingerprint”.

In a further variation of the first embodiment, the apparatus 200 isimplemented in a radio frequency identification (RFID) tag (see FIG. 2c). This embodiment comprises apparatus 200, and also input and outputantennas 203 c, 204 c. The input antenna 203 c is to be electricallyconnected to the nanotubes of the network 202 via input connections 203b. The input antenna 203 c is also configured to be capable of receivingincoming electromagnetic radiation via electromagnetic induction andproviding it to the network 202. The output antenna 204 c is to beelectrically connected to the nanotubes of the network via outputconnections 204 b. The output antenna 204 c is also configured to becapable of translating output signalling from the network 202 intoequivalent emitted electromagnetic radiation.

The RFID tag is assembled by electrically connecting the input andoutput antennas to the input and output connections 203 b, 204 brespectively.

The input antenna 203 c, when receiving an incoming electromagneticsignal, will produce and provide input signalling to the inputconnections 203 b and thereby to the network 202 via electromagneticinduction. The network 202, by virtue of its random disposition, willproduce particular predefined output signalling at the output region204. This output signalling is then communicated via the connectedoutput connections 204 b to the electrically connected output antenna204 c. The output antenna will then emit an output electromagneticsignal in accordance with the output signalling provided by the network202.

This arrangement provides an RFID tag that can produce a unique outputresponse to an input challenge electromagnetic radiation signal. Thiscan be utilised in commercial goods, store goods, shipping containers,shipping pallets, electronics, identification cards, etc, to produceRFID tags that have unique characteristics that can be easily evaluatedand used for identification purposes, but not easily forged. In thisembodiment the RFID tag is passive, but in other embodiments the tag isactive, and in others semi-passive.

The above embodiments can be used for components in low costelectronics. The carbon nanotube networks can even be placed on varioussubstrates, including flexible or transparent substrates.

The physically unclonable function provided by the carbon nanotubenetwork can be easily integrated with electronic circuits, particularlylow cost circuits that are already made of carbon nanotube networks.Furthermore, once it is integrated with other circuitry it can be readelectronically. For example, as a part of an RFID tag (as in theembodiment immediately above) it can be read using RF signals. Thiswould help provide RFID tags that are difficult, or even impossible toforge. In still other examples, this concept could be implemented indevices that would utilise other types of reading equipment, e.g.optical inspection, lasers etc. to identify the unique characteristicsof the network.

We will now describe a second embodiment of the present invention withreference to FIG. 3 a. This embodiment provides an apparatus 300 thatutilises and builds on the apparatus 200 according to FIG. 2 a. Theapparatus 300 also comprises gates 306 and processor 305.

The gates 306 are small metallic plates (on the micro-scale ornano-scale) that are capable of receiving an applied charge or bias inresponse to a control signal. The gates 306 are capable of setting uptheir own corresponding electric field in response to such an appliedbias. The processor 305 is an integrated circuit (IC) chip that iscapable of providing such a control signal to the gates 306 to apply aparticular charge or bias.

The gates 306 are provided on the top surface of the network 302 withthe substrate 301. The gates 306 are configured to be in electricalcommunication with the nanotubes of the network 302, but they are not indirect electrical contact with the nanotubes of the network 302. Thegates 306 are also all electrically connected to the processor 305 sothat the processor can apply the controlled biases to particular gates.

In another variation of this embodiment the substrate is doped and alsoconnected to the processor such that it can also act as a gate,specifically a global gate. This allows the processor to bias the entiresubstrate and thereby bias the entire network (not just local biasing asprovided by the individual gates).

In this embodiment the processor 305 is positioned adjacent to thenetwork/substrate 301, 302 arrangement whilst still remainingelectrically connected to the gates 306. The processor 305 is alsoelectrically connected to input and output regions/connections 303/303b, 304/304 b, to a power source (not shown), and to a memory (notshown).

We will now describe the functionality of this embodiment.

As discussed above, the network 302 will have an effect on providedinput signalling because of its inherent electrical properties. Theexact effect that the network 302 has on the signalling is dependent onthese electrical properties. By altering these electrical properties, itis possible to ‘fine-tune’ or ‘tweak’ these properties.

The processor 305 is configured such that it can provide a controlledbias to the gates 306. The positioning of the gates 306 over the network302 means that when they experience an applied/controlled bias, they setup an electric field that affects/alters the electrical properties ofthe network 302 in that locality. By altering these electricalproperties, the effect of the network 302 on the input signalling can bealtered by using this method.

For example, the network 302 may have a specific function in aparticular locality (in its unbiased state, e.g. an OR function).Normally, when a signal passes through this locality, it is acted on bythis function. In this example, we desire an AND logic function. Byapplying a particular predetermined bias to a particular gate overlyingthat locality, the electrical properties of the network 302 can bealtered to force that OR function acting on the signalling to become anAND function. This may be achieved by recruiting other signal paths, orforcing the flow of charge along a certain path, or preventing aparticular signal path from being taken by the signalling, etc.

As another example, the desired functionality of the network 302 mayrequire that a particular locality be switched between the above ORfunction and the above AND function in certain situations. Byapplication and removal of the applied bias, the function can easily bechanged back and forth between the two functions.

As another example, the desired functionality of the network may requirethat the same locality be switched between a further third logicfunction, e.g. XOR, NOR, etc. This can also be achieved by applying afurther, different predetermined bias to a particular gate 306 to alterthe electrical properties of the network 302 accordingly, in that aparticular function can be changed from a first function (e.g. OR) to asecond function (e.g. AND) and onto a third function (e.g. XOR). Thegates may even provide for four, five, six, seven or more functions tobe selected in a particular locality by way of an applied bias.

As another example, it may be desirable for the network to provide aparticular analog function, i.e. acting as a mathematical operator orproviding a mathematical function to provide output signalling inresponse to input signalling in accordance with the mathematicalfunction. This can enable the apparatus to be used to process analogsignals and not just digital signals (as in the examples utilising logicfunctions). This functionality can enable the apparatus to be used, forexample, as a data classification apparatus, which can receive providedsignalling and identify, rank, order, compare, etc components of thesignalling or signals of the signalling in order to classify them.

The skilled person will appreciate that by application of specificpredetermined biases to gates 306 in specific localities, it is possibleto alter a network 302 to achieve a desired overall functionality. Theprocessor can bias the localised areas of the network to provide any oneor more of these logical or analog functions, or any one or more of themin combination. For example, the network can be tuned to achieve afunctionality that is suitable for a particular task, e.g. calculatorchip, timing chip, half-adder, pattern recognition or dataclassification device. This could be an application specific integratedcircuit provided in a particular device. Also, by virtue of its size,this apparatus could also be a nano-scale processor.

FIGS. 3 c & 3 d show alternative ways of expressing/representing thelogical structures provided by the carbon nanotube networks of thesecond embodiment. Here, the logical network does not necessarilycorrespond directly to the physical network structures of the device,but describes the computational model behind the operation of thedevice. In the computational model, the input and output signallingcorrespond to one another by a predetermined relationship. Thisrelationship is predetermined according to the physical and electricalproperties of the actual network, which can be tuned by applying thebias voltages to the local or global gates. In these figures(particularly FIG. 3 d) the true physical connectivity of the network ismodelled more accurately, with internal nodes (i.e. internal junction orswitching points controlled via the gates) also being shown. Theconnections between internal nodes can be tuned by applying a bias orvoltage to respective gates (i.e. local or global gates). In practice,there will be a large number of internal node connections (connectionsbetween nanowires provided by the nanotubes). As such, using this typemodel to represent the network can become extremely complex, but canallow for more accurate modelling of the operation of such a system.

FIGS. 3 e & 3 f show alternate ways of disposing the structure of thesecond embodiment (or variations of the second, or even variations ofthe first) on a substrate. FIG. 3 e shows one way of disposing thenetwork onto the substrate followed by the gates, whilst FIG. 3 f showsanother way of disposing the gates first (to form ‘back’ gates ratherthan ‘top’ gates) and then the network onto the gates.

FIG. 3 g is provided to illustrate that other types of geometricarchitecture are possible for the embodiments of the present invention,for both the first and second embodiments. This particular figureillustrates how the various embodiments described herein could beprovided in a circular structure. Other types of architecture are alsopossible and within the scope of the application, such as triangles,ellipses, quadrilaterals, regular polygons as well as irregularpolygons, and even multi-layered devices via stacking. The skilledperson will appreciate that many architectural variations forimplementing these embodiments are within the scope of this application.

Different types of these, for example, circular geometries can be usedeither separately or in combination. FIG. 3 g illustrates some of thesepossibilities:

-   -   (1) A carbon nanotube network above a substrate, equal number of        sources and drains and sensors in between.    -   (2) Gates between source-drain pairs and sensors.    -   (3) This type is similar to (2) but with a slightly different        geometry. A combination of (b) and (c) can be used or extended.    -   (4) This utilises sensors of different types, or uses some of        them as a reference when the sensor does not work with an        absolute value but with relative terms.    -   (5) In this example a single source can be used for all drain        electrodes.    -   (6) The gates can be back gates that are positioned below CNTN        (see FIGS. 3 e & 3 f).

In the present second embodiment, the random disposition of the network302 means that the network initially has unknown paths and unknownfunctions inherent within its structure. These will affect inputsignalling in an initially known way. In order to determine thenecessary bias that should be applied to each of the gates 306, theinherent functions of the network 302 must be identified. In thissituation, it can be beneficial to use a learning algorithm.

In a variation of the second embodiment, the processor 305 is alsoprovided with such a learning algorithm. This learning algorithm isconfigured to identify the inherent functionality of the initiallydisposed network 302. It is capable of controlling the biases to beapplied to the gates 306. We will now describe the functionality of thelearning algorithm with reference to FIG. 4.

Step 401: As stated above, the processor 305 is electrically connectedto the input connections 303 b, the output connections 304 b, and thegates 306. The learning algorithm can therefore be run on the processor305 to provide input signalling to the input connections 303 b and testthe corresponding output signalling via the output connections 304 b.The output signalling can be compared to the input signalling to therebyidentify the functions and/or operations provided by the network 302.The resulting determinations can then be stored in the connected memory(not shown). For example, a reference table may be stored in the memoryidentifying the localities in the network 302 having particularfunctions. The inherent functionality of the network 302 is therebyrecorded in the connected memory.

Step 402: Once this has been done, the learning algorithm calculates thebias that would be needed to be applied to particular localities toprovide a particular function. The provision of a new function via theapplied bias may be by configuration/reconfiguration of the functionalready provided by that locality, or it may provide a new functionwhere previously there was no specific function (e.g. reconfiguration ofa path that simply allows signals to pass through unaffected, or a paththat simply does not allow signals to pass, etc). This informationregarding appropriate biases that can be applied, where they are to beapplied, and what function they achieve is then stored in the memory.For example, a further reference table can be stored in which the biasneeded to achieve particular functions in particular localities isrecorded, or the initial table may have such information appended to it.

Step 403: Once this has been done, the processor 305 can utilise thestored information to selectively apply particular biases to providespecific/desired functions throughout the network. For example, in FIG.3 b, a bias applied to one of the top gates can thereby tune thecorresponding locality of the nanotube network 302 so that it becomesinsulating, and thus blocking path(s) from source to drain electrodes303, 304. The processor can also alter/tune the resistivity oflocalities within the network to thereby change and control how thenetwork affects signalling passing through it, (e.g. such that only partof the input signalling will be able to reach the output region).

The skilled person will appreciate that these steps may also beperformed simultaneously or temporally overlapping, as well as beingperformed in sequence. They may even be performed out of sequencedepending on the starting knowledge of the paths of the network 302.

FIG. 3 c is a logical representation of the operation of the apparatusin the second embodiment. The electrical properties of the connectionsand the ‘weights’ of the paths taken between the input and outputregions can be altered by changing the top gate voltages, or they mayput even put to zero. The original connectivity of the network isdefined by the physical properties of the initially manufacturedapparatus, but these properties can be tuned by applying controlledsignals (e.g. applied bias, or voltages). The feedback loops from theoutput lines to the voltages are used, at least in this learningalgorithm embodiment, for teaching the system or even unsupervisedlearning. Using this method, it is possible to take an apparatus havinga completely randomised network structure (as per the above embodiments)and, via the described learning algorithm and gates 306, tune itsproperties to transform the network from an initial state to a differentstate having a desired functionality.

Suitable algorithms can be based on Bayesian networks, or may includeaspects of pattern recognition, data classification, regression analysisand optimization problems.

There are many advantages to this embodiment. As the network is totallyrandom, it is possible to achieve this embodiment without using across-bar structure. Furthermore, providing a randomised networkstructure is very easy in comparison to trying to make a specificnetwork structure. As such, utilising random manufacturing for theseembodiments is a particularly easy and cost effective method.

Also, since in some embodiments the system will be taught specificfunctionality post-manufacture, it is easier to ensure that suchapparatus will be robust against manufacturing defects or variations,which are inherent in any nanoscale fabrication process.

In some embodiments, as the apparatus is taught to have and tuned to acertain functionality, the apparatus will be very efficient for solvingcertain types of computational problems when compared to a similar orequivalent CMOS implementation of identical algorithms. It is alsocapable of acting as an analog device with high speed, being limitedonly by the switching time of the transistors. It can also have very lowenergy consumption.

Due to the achievable size of these apparatus, a large amount ofconnections can be achieved in a very small area. This can lead toenhanced computing capabilities of such networks. The more connectionsone can make, the more complex problems can be solved.

Tuning is done electrically by applying gate voltages, without changingthe physical structure, only its electrical properties. Therefore, it ismore reliable than correcting physical network issues by the use ofsolvents or the like.

Furthermore, the apparatus is particularly well suited to solving andanalyzing sensory data (for example, chemical analysis andidentification).

The method of manufacture can also be done in one step, via dispositionof a network on a known substrate, or sealing within a resin, or thelike. It is therefore a highly efficient method of creating suchstructures.

Shown in FIG. 5 is a method of assembly of the apparatus of FIGS. 1 a &1 b. The method involves disposing the plurality of carbon nanotubeswith said substrate to define the carbon nanotube network. The nanotubesare disposed such that the network is substantially at its percolationthreshold. As a result, the percolation threshold provides for the oneor more signal paths extending from the input region to the outputregion. See step 501. In particular, it is the lengths of the carbonnanotubes and the density of the network that are configured such thatthe network is substantially at the percolation threshold.

This method also forms the basis for constructing the first and secondembodiments, as well as variations thereon.

FIG. 6 illustrates schematically a computer/processor readable media 600providing a program according to the learning algorithm embodiment ofthe present invention. In this example, the computer/processor readablemedia is a disc such as a digital versatile disc (DVD) or a compact disc(CD). In other embodiments, the computer readable media may be any mediathat has been programmed in such a way as to carry out an inventivefunction.

It will be appreciated that the term “signalling” may refer to one ormore signals transmitted as a series of transmitted and/or receivedsignals. The series of signals may comprise one, two, three, four oreven more individual signal components or distinct signals to make upsaid signalling. Some or all of these individual signals may betransmitted/received simultaneously, in sequence, and/or such that theytemporally overlap one another.

It will be appreciated to the skilled reader that any mentionedapparatus/device/server and/or other features of particular mentionedapparatus/device/server may be provided by apparatus arranged such thatthey become configured to carry out the desired operations only whenenabled, e.g. switched on, or the like. In such cases, they may notnecessarily have the appropriate software loaded into the active memoryin the non-enabled (e.g. switched off state) and only load theappropriate software in the enabled (e.g. on state). The apparatus maycomprise hardware circuitry and/or firmware. The apparatus may comprisesoftware loaded onto memory. Such software/computer programs may berecorded on the same memory/processor/functional units and/or on one ormore memories/processors/functional units.

In some embodiments, a particular mentioned apparatus/device/server maybe pre-programmed with the appropriate software to carry out desiredoperations, and wherein the appropriate software can be enabled for useby a user downloading a “key”, for example, to unlock/enable thesoftware and its associated functionality. Advantages associated withsuch embodiments can include a reduced requirement to download data whenfurther functionality is required for a device, and this can be usefulin examples where a device is perceived to have sufficient capacity tostore such pre-programmed software for functionality that may not beenabled by a user.

It will be appreciated that the any mentionedapparatus/circuitry/elements/processor may have other functions inaddition to the mentioned functions, and that these functions may beperformed by the same apparatus/circuitry/elements/processor. One ormore disclosed aspects may encompass the electronic distribution ofassociated computer programs and computer programs (which may besource/transport encoded) recorded on an appropriate carrier (e.g.memory, signal).

It will be appreciated that any “computer” described herein can comprisea collection of one or more individual processors/processing elementsthat may or may not be located on the same circuit board, or the sameregion/position of a circuit board or even the same device. In someembodiments one or more of any mentioned processors may be distributedover a plurality of devices. The same or different processor/processingelements may perform one or more functions described herein.

With reference to any discussion of any mentioned computer and/orprocessor and memory (e.g. including ROM, CD-ROM etc), these maycomprise a computer processor, Application Specific Integrated Circuit(ASIC), field-programmable gate array (FPGA), and/or other hardwarecomponents that have been programmed in such a way to carry out theinventive function.

The applicant hereby discloses in isolation each individual featuredescribed herein and any combination of two or more such features, tothe extent that such features or combinations are capable of beingcarried out based on the present specification as a whole, in the lightof the common general knowledge of a person skilled in the art,irrespective of whether such features or combinations of features solveany problems disclosed herein, and without limitation to the scope ofthe claims. The applicant indicates that the disclosedaspects/embodiments may consist of any such individual feature orcombination of features. In view of the foregoing description it will beevident to a person skilled in the art that various modifications may bemade within the scope of the disclosure.

While there have been shown and described and pointed out fundamentalnovel features of the invention as applied to preferred embodimentsthereof, it will be understood that various omissions and substitutionsand changes in the form and details of the devices and methods describedmay be made by those skilled in the art without departing from thespirit of the invention. For example, it is expressly intended that allcombinations of those elements and/or method steps which performsubstantially the same function in substantially the same way to achievethe same results are within the scope of the invention. Moreover, itshould be recognized that structures and/or elements and/or method stepsshown and/or described in connection with any disclosed form orembodiment of the invention may be incorporated in any other disclosedor described or suggested form or embodiment as a general matter ofdesign choice. Furthermore, in the claims means-plus-function clausesare intended to cover the structures described herein as performing therecited function and not only structural equivalents, but alsoequivalent structures. Thus although a nail and a screw may not bestructural equivalents in that a nail employs a cylindrical surface tosecure wooden parts together, whereas a screw employs a helical surface,in the environment of fastening wooden parts, a nail and a screw may beequivalent structures.

1. An apparatus comprising: a substrate; a plurality of carbon nanotubesdisposed and fixed with said substrate to define a carbon nanotubenetwork substantially at the percolation threshold of the network,wherein the percolation threshold provides for one or more pathsextending from an input region to an output region of the network, theapparatus being configured to, upon receiving particular inputsignalling via the input region, provide particular predefined outputsignalling at the output via the one or more signal paths, theparticular output signalling being predefined according to the one ormore one signal paths.
 2. An apparatus as claimed in claim 1, whereinthe carbon nanotubes are disposed randomly on said substrate to definesaid network, and wherein the particular predefined output signallingcomprises unique output signalling provided by virtue of the randomdistribution of the carbon nanotube network.
 3. An apparatus as claimedin claim 1, wherein the apparatus further comprises one or more gatesconfigured to be in electrical communication with the carbon nanotubenetwork disposed on the substrate, the apparatus also being configuredto be capable of applying bias to one or more of said gates to alter theelectrical properties of the carbon nanotube network to provideparticular predefined output signalling according to the respective biasapplied to one or more of the gates.
 4. The apparatus as claimed inclaim 1, wherein the apparatus further comprises one or more inputconnections and one or more output connections, the input and outputconnections each being respectively configured to be in electricalcommunication with the input and output regions of the carbon nanotubenetwork, the one or more signal paths of the network being configured toextend between respective input connections and corresponding outputconnections, the apparatus being configured to, upon receivingparticular input signalling via one or more of the input connections,provide particular predefined output signalling at one or more of thecorresponding output connections connected via the one or more signalpaths, the particular output signalling being predefined according tothe one or more signal paths connecting the respective input connectionsand corresponding output connections.
 5. The apparatus as claimed inclaim 4, wherein one or more of the signal paths are respectivelyconfigured to provide one or more corresponding functions, theparticular output signalling being predefined according to the one ormore functions provided by the one or more respective signal paths. 6.The apparatus as claimed in claim 3, wherein the apparatus is alsoconfigured to be capable of applying bias to one or more of said gatesto alter the electrical properties of the carbon nanotube network tothereby configure/reconfigure one or more paths to provide one or morerespective functions, the particular predefined output signalling beingpredefined according to the one or more provided/reconfigured functions.7. The apparatus as claimed in claim 6, wherein the apparatus is furtherconfigured to be capable of selectively switching/altering the biasapplied to any one or more of the gates to cause the function providedby any one or more of the corresponding signal paths to be switched froma first function to a second function, wherein the second function isdifferent from the first function.
 8. The apparatus as claimed in claim6, wherein the apparatus is configured to be capable of controlling theone or more gates to configure itself as an application specificintegrated circuit (ASIC).
 9. The apparatus as claimed in claim 1,wherein the apparatus also comprises a processor electrically connectedto one or more of the gates, the input region and the output region,wherein the processor is provided with a learning algorithm configuredto able to perform the method of: detecting functions provided by one ormore signal paths of the network, calculating the applied bias needed toprovide/configure/reconfigure one or more of the signal paths to provideone or more desired functions, and applying the necessary bias to thecorresponding gates to configure the respective one or more signal pathsto provide the one or more desired functions.
 10. A radio frequencyidentification tag comprising the apparatus of claim 2, wherein theinput region is electrically connected to an input antenna, and theoutput region is electrically connected to an output antenna, the radiofrequency identification tag being configured to, upon receivingparticular input signalling via the input antenna, provide uniqueparticular predefined output signalling via the output antenna.
 11. Anano-scale processor comprising the apparatus of claim
 3. 12. Atransistor comprising the apparatus of claim 1, wherein the input regionacts as a source, the output region acts as a drain, and the networktogether with the substrate acts as a semiconductor material providedbetween the source and the drain.
 13. A method for assembling anapparatus, the apparatus comprising: a substrate; a plurality of carbonnanotubes disposed and fixed with said substrate to define a carbonnanotube network substantially at the percolation threshold of thenetwork, wherein the percolation threshold provides for one or morepaths extending from an input region to an output region of the network,the apparatus being configured to, upon receiving particular inputsignalling via the input region, provide particular predefined outputsignalling at the output via the one or more signal paths, theparticular output signalling being predefined according to the one ormore one signal paths, wherein the method comprises: disposing theplurality of the carbon nanotubes with the substrate such that thenetwork is substantially at its percolation threshold, the percolationthreshold thereby providing for the one or more paths extending from theinput region to the output region.
 14. A method for configuring anapparatus, the apparatus comprising: a substrate; a plurality of carbonnanotubes disposed and fixed with said substrate to define a carbonnanotube network substantially at the percolation threshold of thenetwork, wherein the percolation threshold provides for one or morepaths extending from an input region to an output region, the apparatusbeing configured to, upon receiving particular input signalling via theinput region, provide particular predefined output signalling at theoutput via the one or more signal paths, the particular outputsignalling being predefined according to the one or more one signalpaths; wherein the method comprises: detecting functions provided by oneor more signal paths of the network, calculating the applied bias neededto provide/configure/reconfigure one or more of the signal paths toprovide one or more desired functions, and applying the necessary biasto the corresponding gates to configure the respective one or moresignal paths to provide the one or more desired functions.
 15. Acomputer readable medium comprising computer code configured to performthe method of claim 14, when run on a processor comprised by theapparatus.
 16. An apparatus comprising: a substrate; a plurality ofsemiconducting nano-elements disposed and fixed with said substrate todefine a semiconducting network of nano-elements substantially at thepercolation threshold of the network, wherein the percolation thresholdprovides for one or more paths extending from an input region to anoutput region of the network, the apparatus being configured to, uponreceiving particular input signalling via the input region, provideparticular predefined output signalling at the output via the one ormore signal paths, the particular output signalling being predefinedaccording to the one or more one signal paths.